Abstract

AI-driven scientific discovery currently faces two key challenges: enhancing interpretability to extract meaningful insights from data to advance theory, and automating experiments to accelerate progress in the lab. In this talk, I will discuss our efforts to tackle both. First, I will introduce Kolmogorov-Arnold Networks (KANs), which is a fully transparent AI architecture that provides insights into complex physical systems, unlike traditional black-box approaches. In the second part, I will focus on the automation of optical experiments using our AI-driven robotic platform, which integrates an LLM-based agent, a robotic arm, and computer vision for the assembly and alignment of optical setups.